Search Results (29,622)

To accurately assess the seismic risk of bridges, this study systematically conducted probabilistic seismic hazard–fragility–risk assessments using a reinforced concrete continuous girder bridge as a case study. First, the CPSHA method from China’s fifth-generation seismic zoning framework was employed to calculate the Peak Ground Acceleration (PGA) with 2%, 10%, and 63% exceedance probabilities over 50 years as 171.16 gal, 98.10 gal, and 28.61 gal, respectively, classifying the site as being with 0.10 g zone (basic intensity VII). Second, by innovatively integrating the Response Surface Method with Monte Carlo simulation, the study efficiently quantified the coupled effects of structural parameter and ground motion uncertainties, a finite element model was established based on OpenSees, and the seismic fragility curves were plotted. Finally, the risk probability of seismic damage was calculated based on the seismic hazard curve method. The results demonstrate that the study area encompasses 46 potential seismic sources according to China’s fifth-generation zoning. The seismic fragility curves clearly show that side piers and their bearings are generally more susceptible to damage than middle piers and their bearings. Over 50 years, the pier risk probabilities for the intact, slight, moderate, severe damage, and collapse are 68.90%, 6.22%, 15.75%, 7.86%, and 1.27%, while the corresponding probabilities of bearing are 3.54%, 44.11%, 25.64%, 7.74%, and 18.97%, indicating significantly higher bearing risks at the moderate damage and collapse levels. The method proposed in this study is applicable to various types of bridges and has high promotion and application value. Full article

Monitoring large-gradient surface displacement caused by underground mining remains a significant challenge for conventional Synthetic Aperture Radar (SAR)-based techniques. This study introduces optical flow methods to monitor large-gradient displacement in mining areas and conducts a comprehensive comparison with Small Baseline Subset Interferometric SAR (SBAS-InSAR) and Pixel Offset Tracking (POT) methods. Using 12 high-resolution TerraSAR-X (TSX) SAR images over the Daliuta mining area in Yulin, China, we evaluate the performance of each method in terms of sensitivity to displacement gradients, computational efficiency, and monitoring accuracy. Results indicate that SBAS-InSAR is only capable of detecting displacement at the decimeter level in the Dalinta mining area and is unable to monitor rapid, large-gradient displacement exceeding the meter scale. While POT can detect meter-scale displacements, it suffers from low efficiency and low precision. In contrast, the proposed optical flow method (OFM) achieves sub-pixel accuracy with root mean square errors of 0.17 m (compared to 0.26 m for POT) when validated against Global Navigation Satellite System (GNSS) data while improving computational efficiency by nearly 30 times compared to POT. Furthermore, based on the optical flow results, mining parameters and three-dimensional (3D) displacement fields were successfully inverted, revealing maximum vertical subsidence exceeding 4.4 m and horizontal displacement over 1.5 m. These findings demonstrate that the OFM is a reliable and efficient tool for large-gradient displacement monitoring in mining areas, offering valuable support for hazard assessment and mining management. Full article

This study investigated a novel method of recovering energy from iron ore sinter using solid iron oxide heat transfer materials. Traditionally, air is passed through the sinter either in an open conveyor or a sealed vessel to recover energy. The bed materials used were a magnetite concentrate, hematite ore, goethite–hematite ore and sinter fines. A shortwave thermal camera and quartz reactor were used measure infrared radiation from the process. The thermal imaging was combined with image analysis techniques to visualise the transfer of thermal energy through the system. The results showed that energy moved rapidly through the system with peak heating rates of 18 °C/min at a lump sinter temperature of 600 °C. The ratio of heating rate to cooling rate was as high as 8.6:1.0, indicating efficient retention of energy by the bed materials. The bed composition, determined by X-ray fluorescence and X-ray diffraction was used to calculate the heat capacity based on pure material properties. The resultant energy balance determined thermal efficiency to be between 32 and 46% for the sinter fines and hematite–goethite ore, resulting in predicted fuel savings of up to 9.4kg/tonne with similar heat utilisations to the air recovery process. Thermal imaging combined with Brunauer–Emmett–Teller surface area measurements and scanning electron microscopy analysis experimentally replicated mathematical heat transfer model predictions that a smaller total pore volume resulted in less thermally resistive bed. Image analysis illustrated the breaking of the heat front between the less resistive solid and more resistive air in porous beds versus even conduction of heat through a dense bed. The oxide distribution in the bed materials impacted heat transfer, as at a lump temperature of 500 °C was controlled by hydrated oxide content whereas at 600 °C Fe₂O₃ was the more dominant driver. Full article

Since it is difficult to obtain the positions of dynamic loads on structures, this paper suggests a new method to identify the locations of dynamic loads step-by-step based on the correlation coefficients of dynamic responses. First, a recognition model for dynamic load position based on a finite-element scheme is established, with the finite-element domain divided into several regions. Second, virtual loads are applied at the central points of these regions, and acceleration responses are calculated at the sensor measurement points. Third, the maximum correlation coefficient between the calculational and measured accelerations is obtained, and the dynamic load is located in the region with the virtual load corresponding to the maximum correlation coefficient. Finally, this region is continuously subdivided with the refined mesh until the dynamic load is pinpointed in a sufficiently small area. Different virtual load construction methods are proposed according to different types of loads. The frequency response function, unresolvable for the actual problem due to the unknown location of the real dynamic load, can be transformed into a solvable form, involving only known points. This transformation simplifies the analytical process, making it more efficient and applicable to analysis of the dynamic behavior of the system. The identification of the dynamic load position in the entire structure is then transformed into a sub-region approach, focusing on the area where the dynamic load acts. Simulations for case studies are conducted to demonstrate that the proposed method can effectively identify positions of single and multiple dynamic loads. The correctness of the theory and simulation model is verified with experiments. Compared to recent methods that use machine learning and neural networks to identify positions of dynamic loads, the approach proposed in this paper avoids the heavy computational cost and time required for data training. Full article

Effective carbon emission control at the provincial level is essential for advancing the high-quality development of the national economy under the “dual carbon” targets. Although Hubei Province is endowed with abundant natural resources and significant potential for sustainable growth, it still faces considerable challenges in industrial and energy restructuring. Therefore, improving carbon emission efficiency (CEE) is imperative. This study thoroughly analyzes the spatial and temporal characteristics of CEE in Hubei Province. Furthermore, the spatial Durbin model (SDM) and geographically and temporally weighted regression (GTWR) were applied to analyze the determinants of changes in CEE. The results indicate that significant disparities in CEE exist across Hubei Province, with the eastern region exhibiting the highest efficiency and the central region the lowest. The year 2016 represented a turning point, as Moran’s I increased from −0.0006 in 2016 to 0.5134 in 2017, indicating a shift in the spatial pattern of CEE from a weak and insignificant spatial autocorrelation to a strong positive spatial autocorrelation. In addition, the CEE in Hubei Province demonstrated a “siphon effect” and exhibited pronounced polarization. Based on these findings, region-specific policies are proposed. The eastern region should optimize its industrial structure and strengthen urban governance. The western region should leverage its clean energy advantage and enhance carbon sink capacity. The central region should advance low-carbon industrial transformation and coordinated governance to prevent core cities from transferring resources and pollution to surrounding areas. Full article

The growing emphasis on controlling sulfur-containing compounds in fuel oils has driven the development of numerous desulfurization technologies. Among these, catalytic oxidative desulfurization (CODS) has garnered considerable research interest due to its exceptional capability to efficiently remove refractory sulfur compounds, particularly dibenzothiophene (DBT), under relatively mild reaction conditions. However, the widespread application of CODS has been hindered by the high cost and complex preparation processes of the catalysts. To enhance the practical potential of CODS, in this study, a novel Zr@AC catalyst was developed by a facile “solution impregnation + high-temperature calcination” strategy, where zirconium species were effectively supported on activated carbon. Experimental results demonstrated that under optimized conditions of 0.1 g catalyst dosage, 2.0 O/S ratio, reaction temperature 100 °C and reaction time 50 min, the Zr@AC-mediated CODS system achieved a remarkable desulfurization efficiency of 97.24% for DBT removal. The removal efficiency of DBT increased by 9.0% compared with non-catalytic systems. The characterization techniques revealed that the Zr@AC catalyst possesses a hierarchically rough surface morphology, high specific surface area, abundant active sites, and distinctive Zr-O functional groups. Kinetic analysis indicated that the oxidation process follows second-order reaction kinetics. Furthermore, the catalyst maintained over 95% desulfurization efficiency after five consecutive regeneration cycles, confirming that the prepared catalyst has the exceptional recyclability and operational stability. Full article

The increasing emphasis on environmental protection, climate change mitigation, and the transition to a circular economy requires industries, including the wood-processing sector, to integrate sustainability into strategic and operational management. Green growth indicators represent essential tools for evaluating the environmental, economic, and social impacts of business activities, while also contributing to the sustainable economics and responsible management of forest resources and products. This study applies a qualitative research design using structured interviews with 10 executives from medium and large woodworking enterprises in Slovakia. The interviews examined company strategies, practices, and challenges in sustainable development and forest resource utilization. The findings reveal that while many companies actively manage waste, invest in green technologies, and conduct internal audits, the broader implementation of environmental management systems and the uptake of public sustainability funding remain limited. Notably, 90% of respondents emphasized waste volume and recovery rates as critical indicators. Based on the results, a set of green growth indicators was developed and categorized across key thematic areas including waste management, energy efficiency, stakeholder communication, certification, and strategic planning. These indicators not only support the assessment of corporate sustainability but also strengthen efficient forest resource management, responsible use of raw materials, and the long-term economic viability of the sector. The study highlights the importance of systematically designed and practically applicable indicators for guiding companies toward sustainable competitiveness and emphasizes the need for stronger institutional support, improved access to reliable data, and integration of sustainability metrics into core business decision-making. Full article

The conversion of straw biomass into highly graphitized carbon materials is achieved through an efficient molten salt electrolysis process at moderate temperatures (900–950 °C). Increasing the electrolysis temperature significantly enhances the degree of graphitization, structural ordering, and heteroatom removal efficiency, as evidenced by multiscale characterization and electrochemical simulations. The resulting graphitic material exhibits a highly ordered layered structure with improved crystallinity and a larger specific surface area. When used as a potassium-ion battery anode, this biomass-derived carbon delivers a reversible capacity of 232.9 mA·h·g⁻¹ after 100 cycles and retains 230.8 mA·h·g⁻¹ after 500 cycles, owing to its well-developed graphite framework, which accommodates volume changes and facilitates rapid ion diffusion. This study presents a sustainable and scalable strategy for transforming low-cost agricultural waste into high-performance energy storage materials and provides valuable insights into the electrochemical graphitization process. Full article

The article presents the results of research and development work conducted as part of the H2GEO project, aimed at creating a comprehensive technology for the processing of post-mining coal waste heaps. The core of the solution is a mobile density separation system based on a pulsating jig, enabling effective recovery of carbonaceous and mineral fractions. Laboratory experiments assessed the impact of key process parameters—such as sieve slot size, pulsation frequency, and enrichment time—on the efficiency and accuracy of separation for different grain size classes. The most favorable results were obtained using a 2.5 mm screen, a pulsation frequency of 60 min⁻¹, and extended enrichment time, which ensured high-quality separation and low ash content in the carbon-bearing product. The findings supported the design of a new industrial separator (jig) equipped with advanced control systems, facilitating the production of homogeneous fractions suitable for further processing into hydrogen, geopolymers, and construction materials. The proposed solution aligns with circular economy principles, promoting waste reuse, environmental hazard mitigation, and the revitalization of degraded post-industrial areas. Full article

Informal settlements, urban areas with substandard housing conditions and inadequate infrastructure, are increasing in Africa’s sub-Saharan cities, fueled by rapid urbanization, economic challenges, and high housing prices. However, developers often ignore the green building (GB) concept when upgrading housing conditions for these communities. This study aims to investigate GB design strategies specifically for residential structures in Akabahizi to identify and propose practical strategies suitable for informal settlements such as Akabahizi and to develop sustainable housing solutions that enhance environmental quality and meet the needs of residents. Simulation software and combined qualitative and quantitative data collection techniques, including field surveys, interviews, and assessments of existing building conditions, constitute the methodology used in this study. The focus was on the influence of climatic factors, including temperature, precipitation, and wind, on design choices, particularly GB design and current residential buildings in Akabahizi. Based on the survey, 82.5% of residents support the GB concept, 87.4% recognize the importance of GB for community well-being, and 97.1% recognize the benefits of integrating energy-efficient technology for residents’ well-being. Questionnaire findings were considered in decision-making for the design of the new proposed structure to address challenges in the area. Optimized energy efficiency, daylight access, and thermal comfort resulting from courtyard design support GB design incorporating a courtyard as a robust and culturally relevant sustainable design framework tailored for Akabahizi. The courtyard provides green space that promotes social interaction, improves air quality, and delivers natural cooling elements that are essential for residential housing. The proposed new design, with green roof and renewable energy devices, improved material usage, and natural ventilation elements, outperformed the existing one in terms of lower levels of carbon emission for environmental protection. In conclusion, a collaborative effort is needed among various stakeholders, including architects, urban planners, and educational institutions, to promote and implement sustainable building practices. The study suggests that enhancing awareness, offering training opportunities, and empowering local professionals and residents alike can pave the way for improved living conditions and sustainable urban development in Akabahizi and similar informal settlements. Full article

Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the deployment of edge-enabled UAVs using Pareto-PSO is proposed for data collection scenarios in which UAVs operate autonomously and execute onboard distributed multi-objective PSO to maximize the total non-overlapping coverage area while minimizing latency and energy consumption. Performance evaluation is conducted using key indicators, including convergence time, throughput, and total non-overlapping coverage area across bandwidth and swarm-size sweeps. Simulation results demonstrate that the Pareto-PSO consistently attains the highest throughput and the largest coverage envelope, while exhibiting moderate and scalable convergence times. These results highlight the advantage of treating the objectives as a vector-valued objective in Pareto-PSO for real-time, scalable, and energy-aware edge-UAV deployment in dynamic Internet of Flying Things environments. Full article

Soil microorganisms are crucial regulators of wetland ecological functions and are significantly influenced by plants. However, the ecological patterns underlying soil microbial responses to plants during wetland restoration remain poorly understood. Soil samples from sections with and without plants in each wetland were collected to investigate the impact of plants on soil microbial communities using high-throughput absolute quantification sequencing and analysis of soil physicochemical properties. Results showed that environmental drivers exerted stronger effects on microbial communities in areas without plants. Soil microbial networks in areas without plants were more complex and stable, while plants enhanced the contribution of stochastic processes to microbial community assembly. In areas with plants, pH was the most important environmental driver of soil microbial community variations, while organic carbon was the primary driver in areas without plants. Moreover, bacteria exhibited higher sensitivity than fungi to the same environmental variation in both areas with and without plants. In summary, our findings elucidate the responses of soil microbial ecological patterns to plants in newly formed wetlands, while emphasizing that the major environmental drivers of soil microbial communities are influenced by plants. This study provides important implications for enhancing wetland restoration efficiency. Full article

Pavement condition assessment is a fundamental aspect of airport pavement management systems (APMS) for ensuring safe and efficient airport operations. However, conventional methods, which rely on extensive on-site inspections and complex calculations, are often time-consuming and resource-intensive. In response, Industry 4.0 has introduced machine learning (ML) as a powerful tool to streamline these processes. This study explores five ML algorithms (Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM)) for predicting the Pavement Condition Index (PCI). Using basic alphanumeric distress data from three international airports, this study predicts both numerical PCI values (on a 0–100 scale) and categorical PCI values (3 and 7 condition classes). To address data imbalance, random oversampling (SMOTE—Synthetic Minority Oversampling Technique) and undersampling (RUS) were used. This study fills a critical knowledge gap by identifying the most effective algorithms for both numerical and categorical PCI determination, with a particular focus on validating class-based predictions using relatively small data samples. The results demonstrate that ML algorithms, particularly Random Forest, are highly effective at predicting both the numerical and the three-class PCI for the original database. However, accurate prediction of the seven-class PCI required the application of oversampling techniques, indicating that a larger, more balanced database is necessary for this detailed classification. Using 10-fold cross-validation, the successful models achieved excellent performance, yielding Kappa statistics between 0.88 and 0.93, an error rate of less than 7.17%, and an area under the ROC curve greater than 0.93. The approach not only significantly reduces the complexity and time required for PCI calculation, but it also makes the technology accessible, enabling resource-limited airports and smaller management entities to adopt advanced pavement management practices. Full article

Targeted poverty alleviation is a major national initiative in China. The Hotan region, located within the four prefectures of Southern Xinjiang, is one of the 14 contiguous poverty-stricken areas in China as well as a quintessential inland arid zone. Water scarcity is the primary constraint on development in the Hotan region and a major bottleneck for Northwest China as a whole. However, previous assessments of the effectiveness of poverty alleviation measures have primarily focused on industrial growth itself, lacking an analysis of the adaptability between key regional resource elements and industrial poverty alleviation measures. The core of promoting targeted poverty alleviation in arid regions is properly managing the relationships within the “industry–water resources” system and achieving a balance between resource use, environmental capacity, and economic development. Focusing on the coordinated development of industry and water resources, this study evaluates the spatio-temporal evolution of the industry–water resource relationships in the Hotan region after the implementation of the targeted poverty alleviation policy with the aim of measuring the sustainability of industrial poverty alleviation outcomes in this arid region. The results indicate the following: (1) The targeted poverty alleviation policy has reduced industrial water consumption. Following the policy’s implementation, industrial water consumption decreased by 541 million m³, driven by improvements in water use intensity and shifts in the industrial structure. The primary contributor to this reduction was enhanced water use efficiency within the primary sector. (2) The policy exacerbated the misallocation of water resources relative to industrial output across the region. The Gini coefficient for water resources versus GDP across Hotan’s eight counties and cities rose from 0.26 to 0.32, indicating a shift from a ‘relatively balanced’ to a ‘moderately imbalanced’ allocation. Therefore, achieving sustainable poverty alleviation in this arid region necessitates enhanced coordination between industrial development and water resources. Full article

Unmanned aerial vehicles (UAVs) are transforming agriculture through enhanced data acquisition, improved monitoring efficiency, and support for data-driven decision-making. Complementing this, AI-driven platforms provide intuitive and reliable tools for advanced UAV analytics. However, their integration remains underexplored, particularly in specialty crops. Therefore, in this study, we evaluated the performance of an AI-driven web platform (Solvi) for automated plant counting and biometric trait estimation in two contrasting systems: pecan, a perennial nut crop, and onion, an annual vegetable. Ground-truth measurements included pecan tree number, tree height, and canopy area, as well as onion bulb number and diameter, the latter used for market class classification. Counting performance was assessed using precision, recall, and F1 score, while trait estimation was evaluated with linear regression analysis. UAV-based counts showed strong agreement with ground-truth data, achieving precision, recall, and F1 scores above 97% for both crops. For pecans, UAV-derived estimates of tree height (R² = 0.98, error = 11.48%) and canopy area (R² = 0.99, error = 23.16%) demonstrated high accuracy, while errors were larger in young trees compared with mature trees. For onions, UAV-derived bulb diameters achieved an R² of 0.78 with a 6.29% error, and market class classification (medium, jumbo, colossal) was predicted with <10% error. These findings demonstrate that UAV imagery integrated with a user-friendly AI platform can deliver accurate, scalable solutions for biometric monitoring in both perennial and annual specialty crops, supporting applications in harvest planning, orchard management, and market supply forecasting. Full article